Recurrent Neural Network Experts in Germany
in minutes from over 15,000 CVs with the power of AI.Hire experts who build sequence models, tune LSTM and GRU stacks, and ship forecasting or text pipelines with recurrent neural networks. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Recurrent Neural Network
Anjaneya Marimireddygari
Last position:
Machine Learning Engineer Intern at Slash Mark
- Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
- Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
- Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
- Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
- Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.
Sergei Minkov
Last position:
Program Manager / Program Lead (Contractor) at Telefonica
Program Manager for a radical architecture and IT transformation program (RAITT) reshaping the applications landscape (i.e. cloud transformation) and operating model into agile organisation.
- E2E readiness towards mass-market business division covering demand, delivery, test and roll-out phases
- Driving Telefonica internal teams and external system integrators to ensure delivery on time and in quality in adherence to defined processes
- Management of risks, issues and dependencies on program level
Unnikuttan Velamkudy Vijayan
Last position:
Managing Director (Co-Founder) at AathmaSignals
- Spearheading investor outreach and partnership development as founding MD, building the business case and technical narrative needed to attract initial funding and strategic collaborators in the digital health space
- Designing multi-agent AI systems for autonomous biosignal analysis, orchestrating LLM-based reasoning pipelines with domain-specific medical context to enable intelligent, clinical decision support
René Welland
Last position:
Conference Operator at Brähler Systems GmbH
- Developed the iOS/Android Delegate App and the Conference Operator
- Updated and developed a user-friendly conference environment and real-time video streaming
- Optimized the overall conference experience by implementing customizable features for flexible setup
- Enhanced the efficiency and usability of conference technology, enabling a seamless workflow and improved participant interaction experience
Rinaldo Aquino Filho
Last position:
Pricing Tool Coding Development at Kia Corporation
- Pricing Tool Coding Development – Tactical support and further development of a pricing tool solution developed in Visual Basic for use across Europe.
Raghu Ram Vadali
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Harsh Vardhan Agrawal
Last position:
System and Process Integrator 2 at Audi AG
Spearheaded the development and deployment of a Generative AI solution tailored for the automotive industry focusing on improving customer experience through AI-driven innovations.
Conducted in-depth market research to understand unique challenges and opportunities within the automotive sector by analyzing industry trends, customer pain points, and competitive offerings to inform the product strategy.
Formulated a strategic vision for the Generative AI solution targeting personalized customer experiences, aligned product vision with the company’s long-term goals and automotive market demands.
Enhanced customer satisfaction by introducing personalized AI-driven features, achieving a 15% increase in customer engagement and loyalty.
Attended and represented Audi AG on a group-wide level in workshops for AI strategy for customer experience.
Leveraged knowledge of recurrent neural networks and transformer architecture.
Utilized GPT-3 generative AI frameworks.
Employed TensorFlow and PyTorch for machine learning.
Used Tableau from Salesforce for data analysis and visualization.
Served as solution manager for the Business Architecture team.
Collaborated with business stakeholders within Audi OEM to gather requirements for CRM strategy including marketing department, CRM heads across countries, VW group brands and CARIAD SE.
Represented Audi AG in CRM strategy workshops held in different countries.
Discussed CRM strategy with head of CRM and Data based on workshop outcomes.
Conducted business analysis on gathered market data to improve customer experience.
Planned and launched marketing campaigns such as welcome mailing, license renewal reminders, Audi Progress Circle and Black Friday campaigns.
Managed project budget.
Acted as solution manager for the ONE.CRM team at CARIAD SE on loan from Audi AG.
Collaborated with business owners of VW group brands to develop a central solution.
Represented CARIAD SE in CRM strategy workshops in Spain, France and Italy.
Discussed CRM strategy with head of CRM at CARIAD SE based on workshop outcomes.
Conducted business analysis on market and brand data to improve customer experience.
Planned and delivered campaign capabilities from template to brands such as welcome mailing for Audi AG, SEAT and SKODA.
Managed project budget together with head of CRM.
Sara Ali
Last position:
Research Associate and Data Scientist at National Center of Robotics and Automation - Condition Monitoring Lab
- Developed ASR and TSR-based speech processing pipelines on AWS, enabling efficient feature extraction and scalable deployment for speech and text analytics.
- Built a Multimodal Speech Emotion Recognition system combining NLP and deep learning (audio + text), achieving 98% accuracy and supporting real-time, cloud-based inference.
- Designed and optimized end-to-end model training and evaluation workflows using AWS services (S3, EC2, Lambda) to ensure performance, reliability, and reproducibility.
- Created and deployed interactive, user-friendly dashboards for data visualization and insight generation, supporting research teams and management in data-driven decision-making.
Muntaha Shams
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
Aravind Sasi Nair Purayath
Last position:
AI – Data Specialist at Emirates Islamic Bank
- Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
- Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
- Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
- Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
- Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
- Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
- Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
- Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
- Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
- Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
- Integrated testing and CI/CD workflows for robust data pipeline deployment.
Martin Ratajczak
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
Oussama El Allam
Last position:
Head of R&D at eXagotec GmbH
- Spearheading multidisciplinary engineering teams in the development of next-generation medical devices
- Orchestrating research initiatives and technology roadmaps to deliver innovative medical solutions
- Overseeing R&D budget and managing project portfolios from concept through to commercialisation
- Establishing strategic collaborations with clinical partners for technology validation
Borui Li
Last position:
Spectral Analysis of Neural Network Kernels at Borui Li Projects
- Explored the impact of neural network structure on network-inspired kernels, such as Neural Tangent Kernel (NTK).
- Demonstrated through theoretical analysis and empirical studies that the RKHS of NNGP is a subspace of NTK.
- Explored the connections between these kernels and the Matérn family.
Sabrine Krichen
Last position:
Team Lead at InstaDeep
- Led a team of junior Research Engineers, providing mentorship, technical guidance, and career development support to foster their growth in deep learning and machine learning engineering.
Daniel Carton
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Discover over 15,000 top freelancers
Statistics of experts using Recurrent Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2.1 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
95%
Master's degree or higher
86%
Doctorate
14%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using Recurrent Neural Network
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it does
Recurrent Neural Networks, often called RNNs, are built for ordered data. They process one step at a time and keep a hidden state that carries context forward. That makes them useful for language, time series, signals, and any task where earlier inputs affect later ones.
Common uses
- Text prediction and sequence tagging
- Speech and audio analysis
- Demand, sensor, and finance forecasting
- Anomaly detection in streams
- Sequence-to-sequence tasks with encoder-decoder setups
Core models
RNN work often includes LSTM and GRU variants, which handle long dependencies better than a plain RNN. Specialists also know when a bidirectional setup helps and when it adds unnecessary cost. In practice, good choices depend on sequence length, noise, and latency needs.
Tooling
A strong stack usually includes Python, NumPy, pandas, PyTorch, TensorFlow, and Keras. For real projects, experts also handle data windows, masking, batching, and evaluation for imbalanced or drifting sequences. They should know how to trace training issues such as vanishing gradients and unstable loss.
When to hire
Companies bring in freelance expertise when a model must be improved, replaced, or embedded in an existing pipeline. That is common in Germany’s industrial, logistics, automotive, and analytics teams where sequence data is central.
- You have time series data but weak baseline results
- You need LSTM or GRU design choices reviewed
- A prototype must move into production quickly
- Your team needs help with preprocessing and validation
What strong specialists deliver
Strong professionals explain trade-offs clearly and do not treat every sequence problem the same. They build reproducible training runs, choose the right loss and metrics, and make the model fit the business task. They also know when a transformer, CNN, or simpler baseline is the better answer.
Frequently asked questions
What clients ask us most about Recurrent Neural Network — answered in short.
A Recurrent Neural Network is used for data that arrives in order, such as text, audio, signals, and time series. It helps a model use earlier steps as context for later predictions. That makes it useful for forecasting, tagging, sequence classification, and generation tasks.
A Recurrent Neural Network can still be a good fit when sequence length is moderate, latency matters, or the team wants a simpler recurrent setup. Transformers often win on large language tasks, while CNNs can work well for local patterns in sequences. A good specialist will test baselines before committing to one approach.
Yes. Recurrent Neural Network work often includes LSTM and GRU models, which are designed to keep useful context for longer sequences. They are usually preferred over a plain RNN when the data has long dependencies or the training signal is hard to preserve.
A Recurrent Neural Network specialist should also handle data cleaning, sequence preparation, feature scaling, and model evaluation. Python is standard, and PyTorch or TensorFlow is usually expected. Knowledge of deployment, monitoring, and drift handling is valuable when the model must run in production.
A Recurrent Neural Network project is easier to scope when you can define the sequence type, prediction target, data volume, and success metric. A freelancer can still help if the model is only partially defined, but clear access to sample data and pipeline context speeds up the work. The more the data is already prepared, the faster the specialist can focus on modeling.
Yes. Recurrent Neural Network work is often done remotely because the main inputs are data, code, and clear communication. For teams in Germany, remote collaboration works well when data access, language expectations, and review cycles are agreed early. On-site time can still help for sensitive environments or close stakeholder workshops.
A strong Recurrent Neural Network specialist can explain why a plain RNN, LSTM, or GRU was chosen and what was compared against it. Look for clean experiment tracking, sensible validation splits, and a clear link between metrics and the business goal. Good professionals also describe failure modes such as overfitting, leakage, and poor generalization.
A Recurrent Neural Network can fail when the sequence is too long, the data is noisy, or the target is not well aligned with the available history. Problems also appear when preprocessing is inconsistent or the training set does not reflect real production patterns. A careful specialist will check data quality and baseline performance before increasing model complexity.
The average hourly rate of freelancers in Germany who have used Recurrent Neural Network in their recent projects is 83 €, which corresponds to a daily rate of about 661 € based on an 8-hour working day.
Of the freelancers in Germany who have used Recurrent Neural Network in their recent projects, 95% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Germany who have used Recurrent Neural Network in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Germany who have used Recurrent Neural Network in their recent projects are German (100%), English (100%), and French (22%).
The most common industries among freelancers in Germany who have used Recurrent Neural Network in their recent projects are Information Technology (91%), Education (48%), and Healthcare (48%).
The most common business areas among freelancers in Germany who have used Recurrent Neural Network in their recent projects are Information Technology (91%), Product Development (91%), and Research and Development (83%).
Main locations of FRATCH Experts, who have recently used Recurrent Neural Network
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
